水电站巡检数据挖掘与异常检测技术研究

Research on Inspection Data Mining and Anomaly Detection Technology for Hydropower Station

随着智能科技的不断进步,巡检机器人在水电站设施的维护巡检工作中扮演了日益重要的角色。本研究聚焦于一套基于数据挖掘与异常检测技术的数据分析解决方案,对机器人收集的原始巡检数据进行清洗、整合以及特征提炼。运用聚类分析方法对正常运行状态下的设备数据进行模式刻画与模型建构,构建出常态行为基准。采用先进的异常检测算法对可能存在的异常情况进行精准识别与分类。

With the continuous progress of intelligent technology,inspection robots play an increasingly important role in the maintenance and inspection of hydropower station facilities.This study focuses on a set of data analysis solutions based on data mining and anomaly detection technology,then cleans and integrates the original inspection data collected by robots,and refines the characteristics.The cluster analysis method is used for the pattern delineation and model construction of the equipment data under normal operation statues,and the normal behavior benchmark is constructed.Advanced anomaly detection algorithms are used to accurately identify and classify possible anomalies.